Agriculture is the main source of occupation which forms the backbone of our country. It involves the production of crops which may be either food crops or commercial crops. The productivity of crop yield is significantly influenced by various parameters such as rainfall, farm capacity, temperature, crop population density, humidity, irrigation, fertilizer application, solar radiation, type of soil, depth, tillage and soil organic matter. An accurate crop yield prediction support decision-makers in the agriculture sector to predict the yield effectively. Machine learning techniques and deep learning techniques play a significant role in the analysis of data for crop yield prediction. However, the selection of appropriate techniques from the pool of available techniques imposes challenges to the researchers concerning the chosen crop. In this paper, an analysis has been performed on various deep learning and machine learning techniques. To know the limitations of each technique, a comparative analysis is carried out in this paper. In addition to this, a suggestion is provided to further improve the performance of crop yield prediction.

Agriculture, crop yield prediction, productivity of crop yield, machine learning, deep learning

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